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We design a metric learning approach that aims to address computationalchallenges that yield from modeling human outcomes from ambulatory real-lifedata. The proposed metric learning is based on a Siamese neural network (SNN)that learns the relative difference between pairs of samples from a target userand non-target users, thus being able to address the scarcity of labelled datafrom the target. The SNN further minimizes the Wasserstein distance of thelearned embeddings between target and non-target users, thus mitigating thedistribution mismatch between the two. Finally, given the fact that the baserate of focal behaviors is different per user, the proposed method approximatesthe focal base rate based on labelled samples that lay closest to the target,based on which further minimizes the Wasserstein distance. Our method isexemplified for the purpose of hourly stress classification using real-lifemultimodal data from 72 dating couples. Results in few-shot and one-shotlearning experiments indicate that proposed formulation benefits stressclassification and can help mitigate the aforementioned challenges.

A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships